Artificial intelligence in pathology: an overview

Artificial intelligence (AI) is at the forefront of modern technology and emerging uses within the healthcare sector are now being realised. Pathology will be a key area where the impact of AI will be felt. With more and more laboratories making the transition to digital pathology this will provide the key infrastructure in which to deploy these tools and their use will start to become a reality in diagnostic practice. The potential of AI in pathology is to create image analysis tools which could either be used for diagnostic support or to derive novel insights into disease biology, in addition to those achievable with a human observer. Some examples providing diagnostic support currently exist for a limited, but expanding number of applications, such as tumour detection, automated tumour grading, immunohistochemistry scoring, and predicting mutation status. There are a number of challenges to consider, not least the validation and regulatory framework for these tools. In this article, we set out an overview of AI in histopathology, discuss its potential workflow applications, and give key examples of the potential for AI in clinical practice. Considerations for the implementation of AI in practice are also explored.     

Applications and implications of whole-slide imaging in breast pathology

Technological advances in whole slide imaging (WSI) technology and artificial intelligence (AI) applications in recent years have resulted in increasing adoption of this paradigm shift technology. This brings with it many advantages, new challenges, and potential adaptations to the microscopic assessment of specimens that pathologists need to be aware of. This article describes the applications and implications of WSI within the context of the reporting of breast pathology specimens. Challenging diagnostic entities in digital breast pathology are presented and the key areas in which AI could be useful in breast pathology are highlighted.     

Whole-slide imaging in cytopathology: state of the art and future directions

Whole slide imaging (WSI) has been increasingly adopted for digital evaluation of surgical pathology specimens. Unlike histological slides, cytological preparations frequently display a heterogeneous distribution of cells throughout slides in different focal planes sometimes admixed with obscuring material, therefore requiring multiple scanning planes which significantly lengthens image acquisition and evaluation times. Although examination of digital images can be more advantageous than conventional glass slides, the challenges of focusing, scanning and screening cytological specimens and the associated increase in scan times and data storage needs have limited the routine application of WSI in cytopathology practice. Emerging digital systems designed to overcome image acquisition obstacles coupled with artificial intelligence algorithms augmenting screening of digital cytology slides offer innovative solutions to address these limitations. The aim of this review is to critically address the potential benefits and pitfalls of employing WSI in cytopathology practice and to introduce promising state-of-the-art solutions on the horizon.     

Digital workflow management for quality assessment in pathology

Information systems (IS) are well established in the multitude of departments and practices of pathology. Apart from being a collection of doctor's reports, IS can be used to organize and evaluate workflow processes. We report on such a digital workflow management using IS at the Department of Pathology, University Hospital Magdeburg, Germany, and present an evaluation of workflow data collected over a whole year. This allows us to measure workflow processes and to distinguish the effects of alterations in the workflow for quality assessment. Moreover, digital workflow management provides the basis for the integration of diagnostic virtual microscopy.     

Digital breast pathology: validation and training for primary digital practice

Digital pathology is a technology which is transforming the way in which breast histopathology specimens are assessed, reported and reviewed. Large scale clinical laboratory deployments of whole slide imaging systems are occurring in diagnostic pathology departments across the world, requiring laboratory and diagnostic staff to navigate new skills and workflows. Transferring from conventional light microscopy assessment of breast specimens to the use of whole slide images (WSI) can be a challenging experience. This article describes an approach to training and validation for breast consultant histopathologists, which has been used and adapted at a number of sites. Examples of types of case that are suitable for training, and some of the potential “pitfalls” of digital reporting for the novice are described, and practical advice regarding clinical digital breast workflow is shared.     

Digital microbiology

BackgroundDigitalization and artificial intelligence have an important impact on the way microbiology laboratories will work in the near future. Opportunities and challenges lie ahead to digitalize the microbiological workflows. Making efficient use of big data, machine learning, and artificial intelligence in clinical microbiology requires a profound understanding of data handling aspects.ObjectiveThis review article summarizes the most important concepts of digital microbiology. The article gives microbiologists, clinicians and data scientists a viewpoint and practical examples along the diagnostic process.SourcesWe used peer-reviewed literature identified by a PubMed search for digitalization, machine learning, artificial intelligence and microbiology.ContentWe describe the opportunities and challenges of digitalization in microbiological diagnostic processes with various examples. We also provide in this context key aspects of data structure and interoperability, as well as legal aspects. Finally, we outline the way for applications in a modern microbiology laboratory.ImplicationsWe predict that digitalization and the usage of machine learning will have a profound impact on the daily routine of laboratory staff. Along the analytical process, the most important steps should be identified, where digital technologies can be applied and provide a benefit. The education of all staff involved should be adapted to prepare for the advances in digital microbiology.     

Deep learning in digital pathology for personalized treatment plans of cancer patients

Over the past decade, many new cancer treatments have been developed and made available to patients. However, in most cases, these treatments only benefit a specific subgroup of patients, making the selection of treatment for a specific patient an essential but challenging task for oncologists.Although some biomarkers were found to associate with treatment response, manual assessment is time-consuming and subjective. With the rapid developments and expanded implementation of artificial intelligence (AI) in digital pathology, many biomarkers can be quantified automatically from histopathology images. This approach allows for a more efficient and objective assessment of biomarkers, aiding oncologists in formulating personalized treatment plans for cancer patients.This review presents an overview and summary of the recent studies on biomarker quantification and treatment response prediction using hematoxylin-eosin (H&E) stained pathology images. These studies have shown that an AI-based digital pathology approach can be practical and will become increasingly important in improving the selection of cancer treatments for patients.     

Practical immunohistochemistry in hematopathology: a review of useful antibodies for diagnosis

This review article offers some useful panels of immunohistochemical stains and discusses their use in determining a hematopathology diagnosis. As a comprehensive review of the vast array of hematolymphoid malignancies is beyond the scope of this study, the suggestions are based on broad morphologic categories such as follicular proliferations, paracortical expansions, diffuse small-cell infiltrates, diffuse large-cell infiltrates, and Hodgkin-like infiltrates. The review article also discusses the most common hematolymphoid malignancies and their immunohistochemical profiles, and how to use immunophenotyping to differentiate them from other entities. Common diagnostic pitfalls and misconceptions about certain antibodies will also be discussed. New antibodies, such as SOX11, will also be explored in the context of specific disease entities for which they may be of use.     

Fibrocartilaginous mesenchymoma of bone: a single‐institution experience with molecular investigations and a review of the literature

Whole slide imaging is being used increasingly in research applications and in frozen section, consultation and external quality assurance practice. Digital pathology, when integrated with other digital tools such as barcoding, specimen tracking and digital dictation, can be integrated into the histopathology workflow, from specimen accession to report sign‐out. These elements can bring about improvements in the safety, quality and efficiency of a histopathology department. The present paper reviews the evidence for these benefits. We then discuss the challenges of implementing a fully digital pathology workflow, including the regulatory environment, validation of whole slide imaging and the evidence for the design of a digital pathology workstation.     

Artificial intelligence in clinical multiparameter flow cytometry and mass cytometry–key tools and progress

There are many research studies and emerging tools using artificial intelligence (AI) and machine learning to augment flow and mass cytometry workflows. Emerging AI tools can quickly identify common cell populations with continuous improvement of accuracy, uncover patterns in high-dimensional cytometric data that are undetectable by human analysis, facilitate the discovery of cell subpopulations, perform semi-automated immune cell profiling, and demonstrate potential to automate aspects of clinical multiparameter flow cytometric (MFC) diagnostic workflow. Utilizing AI in the analysis of cytometry samples can reduce subjective variability and assist in breakthroughs in understanding diseases.Here we review the diverse types of AI that are being applied to clinical cytometry data and how AI is driving advances in data analysis to improve diagnostic sensitivity and accuracy. We review supervised and unsupervised clustering algorithms for cell population identification, various dimensionality reduction techniques, and their utilities in visualization and machine learning pipelines, and supervised learning approaches for classifying entire cytometry samples.Understanding the AI landscape will enable pathologists to better utilize open source and commercially available tools, plan exploratory research projects to characterize diseases, and work with machine learning and data scientists to implement clinical data analysis pipelines.     

Software-assisted decision support in digital histopathology

Tissue diagnostics is the world of pathologists, and it is increasingly becoming digitalised to leverage the enormous potential of personalised medicine and of stratifying patients, enabling the administration of modern therapies. Therefore, the daily task for pathologists is changing drastically and will become increasingly demanding in order to take advantage of the development of modern computer technologies. The role of pathologist has rapidly evolved from exclusively describing the morphology and phenomenology of a disease, to becoming a gatekeeper for novel and most effective treatment options. This is possible based on the retrieval and management of a wide range of complex information from tissue or a group of cells and associated meta-data. Intelligent and self-learning software solutions can support and guide pathologists to score clinically relevant decisions based on the accurate and robust quantification of multiple target molecules or surrogate biomarker as companion or complimentary diagnostics along with relevant spatial relationships and contextual information from digital H&E and multiplexed images. With the availability of multiplex staining techniques on a single slide, high-resolution image analysis tools, and high-end computer hardware, machine and deep learning solutions now offer diagnostic rulesets and algorithms that still require clinical validation in well-designed studies. Before entering the clinical practice, the ‘human factor’ pathologist needs to develop trust in the output coming from the ‘digital black box of computational pathology’, including image analysis solutions and artificial intelligence algorithms to support critical clinical decisions which otherwise would not be available. © 2020 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.     

Artificial intelligence in digital pathology: a roadmap to routine use in clinical practice

The use of artificial intelligence will transform clinical practice over the next decade and the early impact of this will likely be the integration of image analysis and machine learning into routine histopathology. In the UK and around the world, a digital revolution is transforming the reporting practice of diagnostic histopathology and this has sparked a proliferation of image analysis software tools. While this is an exciting development that could discover novel predictive clinical information and potentially address international pathology workforce shortages, there is a clear need for a robust and evidence-based framework in which to develop these new tools in a collaborative manner that meets regulatory approval. With these issues in mind, the NCRI Cellular Molecular Pathology (CM-Path) initiative and the British In Vitro Diagnostics Association (BIVDA) have set out a roadmap to help academia, industry, and clinicians develop new software tools to the point of approved clinical use. © 2019 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.     

深度学习在数字病理中的应用

临床上,病理切片是癌症诊断的金标准。病理医生通过对病理切片进行镜检,完成病理诊断和预后评估,但是这个过程通常费时费力。在病理切片的数字化的背景下,人工智能技术走进病理领域,并推动病理分析逐渐从定性分析向定量分析转变,这一改变使病理诊断更加准确客观。尤其是以深度学习为代表的人工智能技术在病理分析中取得令人瞩目的成果,不但使病理诊断更加智能化,而且使诊断结果更加精准和客观。阐述深度学习的基本概念及其在数字病理切片分析中的应用,简要概述深度学习在细胞和组织的检测和分割、组织层面上癌症的分类和分级的应用,以及其他一些应用,最后指出目前数字病理切片分析中存在的问题并对未来的发展方向提出展望。     

Artificial intelligence for dermatopathology: Current trends and the road ahead

Artificial intelligence (AI), including deep learning methods that leverage neural network-based algorithms, hold significant promise for dermatopathology and other areas of diagnostic pathology in research and clinical practice. There has been significant progress over past several years in applying AI to analyzing digital histopathology images for diagnosis. While much work in AI analysis of histopathology data remains investigational, recent regulatory agency approval in Europe and United States of AI-assisted tools for clinical use in histopathologic diagnosis of prostate and breast cancer herald broader movement of AI into the clinical diagnostic realm of anatomic pathology, including dermatopathology. However, significant challenges remain in translating AI from research into clinical practice, including algorithmic real-world performance, robustness to variation in data sets and practice settings, effective integration into clinical workflows, and cost effectiveness. This review introduces core concepts and terminology in AI, and assesses current progress and challenges in applying AI to dermatopathology.     

Being fully digital: perspective of a Dutch academic pathology laboratory

The introduction of fast and robust whole slide scanners has facilitated the implementation of ‘digital pathology’ with various uses, the final challenge being full digital diagnostics. In this article, we describe the implementation process of a fully digital workflow for primary diagnostics in 2015 at the University Medical Centre in Utrecht, The Netherlands, as one of the first laboratories going fully digital with a future‐proof complete digital archive. Furthermore, we evaluated the experience of the first 2 years of working with the system by pathologists and residents. The system was successfully implemented in 6 months, including a European tender procedure. Most pathologists and residents had high confidence in working fully digitally, the expertise areas lagging behind being paediatrics, haematopathology, and neuropathology. Reported limitations concerned recognition of microorganisms and mitoses. Neither the age of respondents nor the number of years of pathology experience was correlated with the confidence level regarding digital diagnostics. The ergonomics of digital diagnostics were better than those of traditional microscopy. In this article, we describe our experiences in implementing our fully digital primary diagnostics workflow, describing in depth the implementation steps undertaken, the interlocking components that are required for a fully functional digital pathology system (laboratory management, hospital information systems, data storage, and whole slide scanners), and the changes required in workflow and slide production.     

Image analysis and artificial intelligence in infectious disease diagnostics

BackgroundMicrobiologists are valued for their time-honed skills in image analysis, including identification of pathogens and inflammatory context in Gram stains, ova and parasite preparations, blood smears and histopathologic slides. They also must classify colony growth on a variety of agar plates for triage and assessment. Recent advances in image analysis, in particular application of artificial intelligence (AI), have the potential to automate these processes and support more timely and accurate diagnoses.ObjectivesTo review current AI-based image analysis as applied to clinical microbiology; and to discuss future trends in the field.SourcesMaterial sourced for this review included peer-reviewed literature annotated in the PubMed or Google Scholar databases and preprint articles from bioRxiv. Articles describing use of AI for analysis of images used in infectious disease diagnostics were reviewed.ContentWe describe application of machine learning towards analysis of different types of microbiologic image data. Specifically, we outline progress in smear and plate interpretation as well as the potential for AI diagnostic applications in the clinical microbiology laboratory.ImplicationsCombined with automation, we predict that AI algorithms will be used in the future to prescreen and preclassify image data, thereby increasing productivity and enabling more accurate diagnoses through collaboration between the AI and the microbiologist. Once developed, image-based AI analysis is inexpensive and amenable to local and remote diagnostic use.     

Mapping the Landscape of Care Providers’ Quality Assurance Approaches for AI in Diagnostic Imaging

Journal of Digital Imaging - The discussion on artificial intelligence (AI) solutions in diagnostic imaging has matured in recent years. The potential value of AI adoption is well established, as...     

Going glass to digital: virtual microscopy as a simulation-based revolution in pathology and laboratory science

The recent technological advance of digital high resolution imaging has allowed the field of pathology and medical laboratory science to undergo a dramatic transformation with the incorporation of virtual microscopy as a simulation-based educational and diagnostic tool. This transformation has correlated with an overall increase in the use of simulation in medicine in an effort to address dwindling clinical resource availability and patient safety issues currently facing the modern healthcare system. Virtual microscopy represents one such simulation-based technology that has the potential to enhance student learning and readiness to practice while revolutionising the ability to clinically diagnose pathology collaboratively across the world. While understanding that a substantial amount of literature already exists on virtual microscopy, much more research is still required to elucidate the full capabilities of this technology. This review explores the use of virtual microscopy in medical education and disease diagnosis with a unique focus on key requirements needed to take this technology to the next level in its use in medical education and clinical practice.     

Digital health,big data and smart technologies for the care of patients with systemic autoimmune diseases: Where do we stand?

The past decade has seen tremendous development in digital health, including in innovative new technologies such as Electronic Health Records, telemedicine, virtual visits, wearable technology and sophisticated analytical tools such as artificial intelligence (AI) and machine learning for the deep-integration of big data. In the field of rare connective tissue diseases (rCTDs), these opportunities include increased access to scarce and remote expertise, improved patient monitoring, increased participation and therapeutic adherence, better patient outcomes and patient empowerment. In this review, we discuss opportunities and key-barriers to improve application of digital health technologies in the field of autoimmune diseases. We also describe what could be the fully digital pathway of rCTD patients. Smart technologies can be used to provide real-world evidence about the natural history of rCTDs, to determine real-life drug utilization, advanced efficacy and safety data for rare diseases and highlight significant unmet needs. Yet, digitalization remains one of the most challenging issues faced by rCTD patients, their physicians and healthcare systems. Digital health technologies offer enormous potential to improve autoimmune rCTD care but this potential has so far been largely unrealized due to those significant obstacles. The need for robust assessments of the efficacy, affordability and scalability of AI in the context of digital health is crucial to improve the care of patients with rare autoimmune diseases.